While SIFT [1]} and SURF [2]} have been invented almost 20 years ago, they are still considered by many to be state-of-the-art feature matching techniques.
One research group found SIFT to be the second-best descriptor in terms of feature matching and recognition accuracy of the same object or scene [3]}.
Gradient Location and Orientation Histogram (GLOH), their proposed extension of SIFT, marginally outperformed SIFT for most tasks.
This shows the robustness and distinctiveness of SIFT descriptors.
A review found that SIFT and SURF converged similarly to the lowest errors for monocular visual-odometry tasks [4]}.
Another group concluded that SIFT is still the most accurate feature descriptor for image recognition and feature matching applications [5]}.
They also found that general-purpose feature descriptors perform fairly well for feature matching but are not good for image recognition.
Some researchers compared SIFT descriptors with descriptors obtained using a pre-trained Convolutional Neural Network (CNN) on the Imagenet dataset [6]}.
They found that while CNN-based methods have advantages in nearly all their benchmarking datasets, SIFT is still better in the cases where images are gray-scale, there are intense color changes, and when there are severe occlusions.
One review concluded that SIFT is the most accurate feature-detector-descriptor for overall scale, rotations, and affine deformations [7]}.
We think this is due to the CNN not having these types of instances in its training distribution.
In the past 2 years, SIFT and SURF have been used in many applications, like underwater image recognition for autonomous underwater vehicles [8]}, image forgery detection [9]}, [10]}, and mosaicking of unmanned aerial vehicle images for crop growth monitoring [11]}.
In 2017, SIFT was even used for currency recognition [12]}.
